Arbeitspapier
Low-rank approximations of nonseparable panel models
We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrixcompletion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-difference approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP52/20
- Classification
-
Wirtschaft
- Event
-
Geistige Schöpfung
- (who)
-
Fernández-Val, Iván
Freeman, Hugo
Weidner, Martin
- Event
-
Veröffentlichung
- (who)
-
Centre for Microdata Methods and Practice (cemmap)
- (where)
-
London
- (when)
-
2020
- DOI
-
doi:10.47004/wp.cem.2020.5220
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Fernández-Val, Iván
- Freeman, Hugo
- Weidner, Martin
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2020
Other Objects (12)
Low-rank approximations of nonseparable panel models
Low‐rank approximations of frequency‐limited discrete‐time Gramians
Low-rank Parareal: a low-rank parallel-in-time integrator
From low-rank retractions to dynamical low-rank approximation and back
Low-rank Tensor Recovery
Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold
Dynamical low‐rank approximations of solutions to the Hamilton–Jacobi–Bellman equation
Learning-based low-rank denoising
A Low-Rank Matrix Approach to Compute Polynomial Approximations of Smooth Two-Dimensional Functions
Random perturbation of low-rank matrices
Multilinear optimization in low-rank models
Adjoints and low-rank covariance representation
Low-rank approximations of nonseparable panel models
Low‐rank approximations of frequency‐limited discrete‐time Gramians
Low-rank Parareal: a low-rank parallel-in-time integrator
From low-rank retractions to dynamical low-rank approximation and back
Low-rank Tensor Recovery
Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold
Dynamical low‐rank approximations of solutions to the Hamilton–Jacobi–Bellman equation
Learning-based low-rank denoising
A Low-Rank Matrix Approach to Compute Polynomial Approximations of Smooth Two-Dimensional Functions
Random perturbation of low-rank matrices
Multilinear optimization in low-rank models
Adjoints and low-rank covariance representation
Low-rank approximations of nonseparable panel models
Low‐rank approximations of frequency‐limited discrete‐time Gramians
Low-rank Parareal: a low-rank parallel-in-time integrator
From low-rank retractions to dynamical low-rank approximation and back
Low-rank Tensor Recovery
Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold
Dynamical low‐rank approximations of solutions to the Hamilton–Jacobi–Bellman equation
Learning-based low-rank denoising
A Low-Rank Matrix Approach to Compute Polynomial Approximations of Smooth Two-Dimensional Functions
Random perturbation of low-rank matrices
Multilinear optimization in low-rank models